Actualized Intelligence: Case-based Agency in Practice

Researchers who build autonomous agents are primarily interested in integrating the aspects of agency which were once thought decomposable and independent. A theory of agency for everyday worlds must address the problems that an agent has a limited time to plan, and that an agent must be able to cope with an incomplete domain physics. The Case-based Agency projects at the AI Lab at the University of Chicago demonstrate how reasoning from memory provides a complete framework for a theory of agency. The three projects described in this paper revolve around a single point. Plans in memory are the most valuable resource for responsive and adaptive activity in everyday worlds. Case-based reasoning provides an approach which answers the fundamental problems of a theory of agency. This paper discusses three techniques for eeective leveraging of plans in memory for use in everyday worlds: stabilization, learning from failure, and regularity-seeking perception.